This contrasts with other segmentation networks, which are based on a set of small, single-size kernels [1]}, [2]}. After the input convolutional block, six more convolutional blocks are used. All the two-scale multi-kernel blocks employ 3\(\times \) 3 and 5\(\times \) 5 filters. Altogether, the number of convolutional layers is small and, as a result, the network is shallow, with a low computational complexity and small number of trainable parameters. While MKIS-Net is a shallow network, it adds in an element-wise fashion the receptive fields produced by each of the multi-kernel blocks. This has the effect of creating a multiscale feature map comprised by the receptive field of kernels of different sizes. Notice that our architecture is devoid of pooling layers. Therefore, to control the feature map size, we employ two strided convolutions, which yield a feature map size reduction by half. At the output stage, transposed convolutions and a softmax layer are used. As dropout helps to reduce the issues of over-fitting arising from shallow networks [3]}, [4]}, we have used dropout in the output block with a probability of 0.4. In our network we employ a classification layer to deliver a categorical label for each image pixel.
